# Suppress warnings for cleaner outputs
import warnings; warnings.filterwarnings("ignore")
import numpy as np
np.random.seed(42)

import pandas as pd
url = 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv'
cols = ['Pregnancies','Glucose','BloodPressure','SkinThickness','Insulin','BMI','DiabetesPedigree','Age','Outcome']
df = pd.read_csv(url, names=cols)
print(df.shape)
print(df.head(3))

# Count zeros in key columns
for col in ['Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI']:
    print(f"{col}: {(df[col] == 0).sum()} zeros")
    

# Replace zeros with NaN, then fill with median
import numpy as np
for col in ['Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI']:
    df[col] = df[col].replace(0, np.nan)
    df[col].fillna(df[col].median(), inplace=True)
print(df.isnull().sum())

# Value counts for the target variable
print(df['Outcome'].value_counts())

# Quick plot of class balance
import matplotlib.pyplot as plt
df['Outcome'].value_counts().plot(kind='bar', color=['skyblue','orchid'])
plt.xlabel('Diabetes Present (1=Yes, 0=No)')
plt.title('Class Balance for Diabetes')
plt.show()

# Features and target split
X = df.drop('Outcome', axis=1)
y = df['Outcome']

# Train-test split
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
print(X_train.shape, X_test.shape)

# AdaBoost setup
from sklearn.ensemble import AdaBoostClassifier
model_ada = AdaBoostClassifier(n_estimators=50, random_state=42)
model_ada.fit(X_train, y_train)

# AdaBoost prediction and accuracy
from sklearn.metrics import accuracy_score
y_pred_ada = model_ada.predict(X_test)
score_ada = accuracy_score(y_test, y_pred_ada)
print(f"AdaBoost Test Accuracy: {score_ada:.2f}")

# XGBoost setup
import xgboost as xgb
model_xgb = xgb.XGBClassifier(use_label_encoder=False, eval_metric='logloss', random_state=42)
model_xgb.fit(X_train, y_train)

# XGBoost prediction and accuracy
y_pred_xgb = model_xgb.predict(X_test)
score_xgb = accuracy_score(y_test, y_pred_xgb)
print(f"XGBoost Test Accuracy: {score_xgb:.2f}")

# Plot feature importances from XGBoost
import seaborn as sns
importances = model_xgb.feature_importances_
features = X.columns
sns.barplot(x=importances, y=features, palette='Blues_r')
plt.title('Feature Importance from XGBoost')
plt.show()

# Confusion matrix for XGBoost
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
cm = confusion_matrix(y_test, y_pred_xgb)
disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model_xgb.classes_)
disp.plot(cmap='Oranges')
plt.title('XGBoost Confusion Matrix')
plt.show()

# Ask the user to try predicting a patient
sample = X_test.iloc[0:1]
print('Patient data:')
print(sample)
user_input = input('Do you think this patient will have diabetes? Enter 1 for Yes, 0 for No: ')
xgb_pred = model_xgb.predict(sample)[0]
print(f'XGBoost predicts: {xgb_pred}')
print('Did you and the model agree?')

# Quick challenge: Can you tune XGBoost learning rate?
model_xgb2 = xgb.XGBClassifier(use_label_encoder=False, eval_metric='logloss', learning_rate=0.1, random_state=42)
model_xgb2.fit(X_train, y_train)
score_xgb2 = accuracy_score(y_test, model_xgb2.predict(X_test))
print(f"XGBoost (learning_rate=0.1) Accuracy: {score_xgb2:.2f}")